code: def load_cora_data():
      data = citegrh.load_cora()
      g = data[0]
      mask = torch.BoolTensor(g.ndata["train_mask"])
      return g, g.ndata["feat"], g.ndata["label"], mask
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  import networkx
  from dgl import DGLGraph
  from dgl.data import citation_graph
  import time
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: load_cora_data() and returns: g,  g,  g,  mask